{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2018 D. Koehn, notebook style sheet by L.A. Barba, N.C. Clementi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<link href=\"https://fonts.googleapis.com/css?family=Merriweather:300,300i,400,400i,700,700i,900,900i\" rel='stylesheet' >\n",
       "<link href=\"https://fonts.googleapis.com/css?family=Source+Sans+Pro:300,300i,400,400i,700,700i\" rel='stylesheet' >\n",
       "<link href='http://fonts.googleapis.com/css?family=Source+Code+Pro:300,400' rel='stylesheet' >\n",
       "<style>\n",
       "\n",
       "@font-face {\n",
       "    font-family: \"Computer Modern\";\n",
       "    src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
       "}\n",
       "\n",
       "\n",
       "#notebook_panel { /* main background */\n",
       "    background: rgb(245,245,245);\n",
       "}\n",
       "\n",
       "div.cell { /* set cell width */\n",
       "    width: 800px;\n",
       "}\n",
       "\n",
       "div #notebook { /* centre the content */\n",
       "    background: #fff; /* white background for content */\n",
       "    width: 1000px;\n",
       "    margin: auto;\n",
       "    padding-left: 0em;\n",
       "}\n",
       "\n",
       "#notebook li { /* More space between bullet points */\n",
       "margin-top:0.5em;\n",
       "}\n",
       "\n",
       "/* draw border around running cells */\n",
       "div.cell.border-box-sizing.code_cell.running { \n",
       "    border: 1px solid #111;\n",
       "}\n",
       "\n",
       "/* Put a solid color box around each cell and its output, visually linking them*/\n",
       "div.cell.code_cell {\n",
       "    background-color: rgb(256,256,256); \n",
       "    border-radius: 0px; \n",
       "    padding: 0.5em;\n",
       "    margin-left:1em;\n",
       "    margin-top: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "div.text_cell_render{\n",
       "    font-family: 'Source Sans Pro', sans-serif;\n",
       "    line-height: 140%;\n",
       "    font-size: 110%;\n",
       "    width:680px;\n",
       "    margin-left:auto;\n",
       "    margin-right:auto;\n",
       "}\n",
       "\n",
       "/* Formatting for header cells */\n",
       ".text_cell_render h1 {\n",
       "    font-family: 'Merriweather', serif;\n",
       "    font-style:regular;\n",
       "    font-weight: bold;    \n",
       "    font-size: 250%;\n",
       "    line-height: 100%;\n",
       "    color: #004065;\n",
       "    margin-bottom: 1em;\n",
       "    margin-top: 0.5em;\n",
       "    display: block;\n",
       "}\t\n",
       ".text_cell_render h2 {\n",
       "    font-family: 'Merriweather', serif;\n",
       "    font-weight: bold; \n",
       "    font-size: 180%;\n",
       "    line-height: 100%;\n",
       "    color: #0096d6;\n",
       "    margin-bottom: 0.5em;\n",
       "    margin-top: 0.5em;\n",
       "    display: block;\n",
       "}\t\n",
       "\n",
       ".text_cell_render h3 {\n",
       "    font-family: 'Merriweather', serif;\n",
       "\tfont-size: 150%;\n",
       "    margin-top:12px;\n",
       "    margin-bottom: 3px;\n",
       "    font-style: regular;\n",
       "    color: #008367;\n",
       "}\n",
       "\n",
       ".text_cell_render h4 {    /*Use this for captions*/\n",
       "    font-family: 'Merriweather', serif;\n",
       "    font-weight: 300; \n",
       "    font-size: 100%;\n",
       "    line-height: 120%;\n",
       "    text-align: left;\n",
       "    width:500px;\n",
       "    margin-top: 1em;\n",
       "    margin-bottom: 2em;\n",
       "    margin-left: 80pt;\n",
       "    font-style: regular;\n",
       "}\n",
       "\n",
       ".text_cell_render h5 {  /*Use this for small titles*/\n",
       "    font-family: 'Source Sans Pro', sans-serif;\n",
       "    font-weight: regular;\n",
       "    font-size: 130%;\n",
       "    color: #e31937;\n",
       "    font-style: italic;\n",
       "    margin-bottom: .5em;\n",
       "    margin-top: 1em;\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".text_cell_render h6 { /*use this for copyright note*/\n",
       "    font-family: 'Source Code Pro', sans-serif;\n",
       "    font-weight: 300;\n",
       "    font-size: 9pt;\n",
       "    line-height: 100%;\n",
       "    color: grey;\n",
       "    margin-bottom: 1px;\n",
       "    margin-top: 1px;\n",
       "}\n",
       "\n",
       "    .CodeMirror{\n",
       "            font-family: \"Source Code Pro\";\n",
       "\t\t\tfont-size: 90%;\n",
       "    }\n",
       "/*    .prompt{\n",
       "        display: None;\n",
       "    }*/\n",
       "\t\n",
       "    \n",
       "    .warning{\n",
       "        color: rgb( 240, 20, 20 )\n",
       "        }  \n",
       "</style>\n",
       "<script>\n",
       "    MathJax.Hub.Config({\n",
       "                        TeX: {\n",
       "                           extensions: [\"AMSmath.js\"], \n",
       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
       "                           },\n",
       "                tex2jax: {\n",
       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
       "                },\n",
       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
       "                \"HTML-CSS\": {\n",
       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
       "                }\n",
       "        });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute this cell to load the notebook's style sheet, then ignore it\n",
    "from IPython.core.display import HTML\n",
    "css_file = '../style/custom.css'\n",
    "HTML(open(css_file, \"r\").read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mesh generation by Transfinite Interpolation applied to the sea dike problem\n",
    "\n",
    "We have implemented and tested our mesh generation approach using Transfinite Interpolation (TFI) in the previous lesson. Now, let's apply it to the problem of the sea dike with strong topography."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Revisiting the sea dike problem\n",
    "\n",
    "To generate a deformed quad mesh incorporating the strong topography of the sea dike, we only have to describe the topography by a parametrized curve. We can roughly describe it by the following equations:\n",
    "\n",
    "* $x = 0\\; m - 4\\; m\\; \\rightarrow\\; z(x) = 0\\; m$\n",
    "* $x = 4\\; m - 18.5\\; m\\; \\rightarrow\\; z(x) = \\frac{6.76}{14.5}(x-4)\\; m$\n",
    "* $x = 18.5\\; m - 22.5\\; m\\; \\rightarrow\\; z(x) = 6.76\\; m$\n",
    "* $x = 22.5\\; m - 44.17\\; m\\; \\rightarrow\\; z(x) = -\\frac{3.82}{21.67}(x-22.5)\\; m$\n",
    "\n",
    "This might be a little bit rough approximation, because photos of the data acquisition show a smooth transition between the tilted and horizontal surfaces of the dike. Nevertheless, let's try to generate a mesh for this topography model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Here, I introduce a new library, which is useful \n",
    "# to define the fonts and size of a figure in a notebook\n",
    "from pylab import rcParams\n",
    "\n",
    "# Get rid of a Matplotlib deprecation warning\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define number of grid points in x-direction and spatial vectors\n",
    "NXtopo = 100\n",
    "x_dike = np.linspace(0.0, 61.465, num=NXtopo)\n",
    "z_dike = np.zeros(NXtopo)\n",
    "\n",
    "# calculate dike topograpy\n",
    "def dike_topo(x_dike, z_dike, NX1):\n",
    "    \n",
    "    for i in range(NX1):\n",
    "\n",
    "        if(x_dike[i]<4.0):\n",
    "            z_dike[i] = 0.0\n",
    "        \n",
    "        if(x_dike[i]>=4.0 and x_dike[i]<18.5):\n",
    "            z_dike[i] = (x_dike[i]-4) * 6.76/14.5\n",
    "        \n",
    "        if(x_dike[i]>=18.5 and x_dike[i]<22.5):\n",
    "            z_dike[i] = 6.76\n",
    "    \n",
    "        if(x_dike[i]>=22.5 and x_dike[i]<x_dike[-1]):\n",
    "            z_dike[i] = -(x_dike[i]-22.5) * 3.82/21.67 + 6.76\n",
    "            \n",
    "    return x_dike, z_dike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define figure size\n",
    "rcParams['figure.figsize'] = 10, 7\n",
    "\n",
    "# Plot sea dike topography\n",
    "dike_topo(x_dike,z_dike,NXtopo)\n",
    "plt.plot(x_dike,z_dike)\n",
    "plt.title(\"Sea dike topography\" )\n",
    "plt.xlabel(\"x [m]\")\n",
    "plt.ylabel(\"z [m]\")\n",
    "plt.axes().set_aspect('equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, the TFI is defined on the unit square, so we have to normalize the sea dike topography, before applying the TFI. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize sea dike topography\n",
    "xmax_dike = np.max(x_dike)\n",
    "zmax_dike = np.max(z_dike)\n",
    "\n",
    "x_dike_norm = x_dike / xmax_dike\n",
    "z_dike_norm = z_dike / zmax_dike + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot normalized sea dike topography\n",
    "plt.plot(x_dike_norm,z_dike_norm)\n",
    "plt.title(\"Normalized sea dike topography\" )\n",
    "plt.xlabel(\"x []\")\n",
    "plt.ylabel(\"z []\")\n",
    "plt.axes().set_aspect('equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, now we have the normalized dike topography on a unit square, so we can define the parametric curve for the topography."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Define parameters for deformed Cartesian mesh\n",
    "NX = 80\n",
    "NZ = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define parametric curves at model boundaries ...\n",
    "# ... bottom boundary\n",
    "def Xb(s):\n",
    "    \n",
    "    x = s\n",
    "    z = 0.0\n",
    "\n",
    "    xzb = [x,z]\n",
    "    \n",
    "    return xzb\n",
    "\n",
    "# ... top boundary\n",
    "def Xt(s):\n",
    "    \n",
    "    x = s\n",
    "    \n",
    "    # normalized x-coordinate s -> unnormalized x-coordinate x_d\n",
    "    x_d = xmax_dike * s\n",
    "    z_d = 0.0\n",
    "    \n",
    "    if(x_d<4.0):\n",
    "        z_d = 0.0\n",
    "        \n",
    "    if(x_d>=4.0 and x_d<18.5):\n",
    "        z_d = (x_d-4) * 6.76/14.5\n",
    "        \n",
    "    if(x_d>=18.5 and x_d<22.5):\n",
    "        z_d = 6.76\n",
    "    \n",
    "    if(x_d>=22.5 and x_d<xmax_dike):\n",
    "        z_d = -(x_d-22.5) * 3.82/21.67 + 6.76\n",
    "    \n",
    "    # unnormalized z-coordinate z_d -> normalized z-coordinate z\n",
    "    z = z_d / zmax_dike + 1\n",
    "\n",
    "    xzt = [x,z]\n",
    "    \n",
    "    return xzt\n",
    "\n",
    "# ... left boundary\n",
    "def Xl(s):\n",
    "    \n",
    "    x = 0.0\n",
    "    z = s  \n",
    "\n",
    "    xzl = [x,z]\n",
    "    \n",
    "    return xzl\n",
    "\n",
    "# ... right boundary\n",
    "def Xr(s):\n",
    "    \n",
    "    x = 1\n",
    "    z = s\n",
    "    \n",
    "    xzr = [x,z]\n",
    "    \n",
    "    return xzr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transfinite interpolation\n",
    "\n",
    "# Discretize along xi and eta axis\n",
    "xi = np.linspace(0.0, 1.0, num=NX)\n",
    "eta = np.linspace(0.0, 1.0, num=NZ)\n",
    "\n",
    "xi1, eta1 = np.meshgrid(xi, eta)\n",
    "\n",
    "# Intialize matrices for x and z axis\n",
    "X = np.zeros((NX,NZ))\n",
    "Z = np.zeros((NX,NZ))\n",
    "\n",
    "# loop over cells\n",
    "for i in range(NX):\n",
    "    Xi = xi[i]\n",
    "    for j in range(NZ):\n",
    "        Eta = eta[j]\n",
    "        \n",
    "        xb = Xb(Xi)\n",
    "        xb0 = Xb(0)\n",
    "        xb1 = Xb(1)\n",
    "        \n",
    "        xt = Xt(Xi)\n",
    "        xt0 = Xt(0)\n",
    "        xt1 = Xt(1)\n",
    "        \n",
    "        xl = Xl(Eta)\n",
    "        xr = Xr(Eta)\n",
    "\n",
    "        # Transfinite Interpolation (Gordon-Hall algorithm)\n",
    "        X[i,j] = (1-Eta) * xb[0] + Eta * xt[0] + (1-Xi) * xl[0] + Xi * xr[0] \\\n",
    "               - (Xi * Eta * xt1[0] + Xi * (1-Eta) * xb1[0] + Eta * (1-Xi) * xt0[0] \\\n",
    "               + (1-Xi) * (1-Eta) * xb0[0])\n",
    "            \n",
    "        Z[i,j] = (1-Eta) * xb[1] + Eta * xt[1] + (1-Xi) * xl[1] + Xi * xr[1] \\\n",
    "               - (Xi * Eta * xt1[1] + Xi * (1-Eta) * xb1[1] + Eta * (1-Xi) * xt0[1] \\\n",
    "               + (1-Xi) * (1-Eta) * xb0[1])        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No error so far. Before plotting the generated mesh, we have to unnormalize the spatial coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unnormalize the mesh \n",
    "X = X * xmax_dike\n",
    "Z = Z * zmax_dike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot TFI mesh (physical domain)\n",
    "plt.plot(X, Z, 'k')\n",
    "plt.plot(X.T, Z.T, 'k')\n",
    "\n",
    "plt.title(\"Sea dike TFI grid (physical domain)\" )\n",
    "plt.xlabel(\"x [m]\")\n",
    "plt.ylabel(\"z [m]\")\n",
    "plt.axes().set_aspect('equal')\n",
    "plt.savefig('sea_dike_TFI.pdf', bbox_inches='tight', format='pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we have a deformed quad mesh for the sea dike problem, representing the strong surface topography quite well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We learned:\n",
    "\n",
    "* How to generate a deformed quad mesh by transfinite interpolation for a sea dike topography\n",
    "* Quad mesh generation is quite time-consuming, even for simple model geometries\n",
    "* What Post-Docs do in their free-time"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
